Examining the Ethical Implications and Potential Biases in Computer Vision Machine Learning Algorithms and Their Societal Impact

Abstract

As computer vision machine learning (ML) algorithms become more sophisticated and widely deployed, it is critical to examine their ethical implications and potential for introducing or amplifying societal biases. This paper investigates key ethical considerations surrounding computer vision ML, including issues of fairness, accountability, transparency, privacy, and potential for misuse. It analyzes how biases can be introduced at various stages of the ML pipeline and discusses real-world examples where computer vision has demonstrated bias along lines of race, gender, age and other attributes. The broader impacts on society are explored, including how biased algorithms can lead to disparate or discriminatory outcomes in domains like law enforcement, hiring, healthcare and beyond. Finally, the paper surveys existing approaches to mitigating bias, improving transparency and ensuring more ethical development of computer vision ML. It argues that proactively addressing these challenges is essential as computer vision becomes infused in ever more aspects of society.